Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.

中文翻译：

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一种基于光梯度增强机的用于预测药物与疾病关联的图表示方法。

确定药物与疾病之间的关联对于药物开发非常重要。然而，现有的药物-疾病关联（DDA）预测方法在特征提取方面过于同质。在这里，提出了一种基于光梯度增强机（GRLGB）的新颖图表示方法来预测 DDA。将蛋白质引入异构网络后，从网络拓扑和生物知识两个角度提取节点特征。最后，应用 GRLGB 分类器来预测潜在的 DDA。GRLGB通过10倍交叉验证在Bdataset和Fdataset上取得了令人满意的结果。为了进一步证明GRLGB的可靠性，进行了涉及焦虑症和氯氮平的案例研究。结果表明 GRLGB 可以识别新型 DDA。